MS in Machine Learning Online

MS in Information Science: Machine Learning

No GRE/ TOEFL required*
Save USD 67,750
Upto 3 years STEM OPT Visa in the US

*Applicable for selected countries only

Online/Hybrid • 2 years

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Enquire: +1 520 341 2109

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Program Delivered by:

In Collaboration with:

U.S. News & World Report
RANKINGS AND RECOGNITIONS TOP 50 PUBLIC UNIVERSITY

U.S. News & World Report

2022

U.S. News & World Report
TOP 100 GLOBAL UNIVERSITY

U.S. News & World Report

2021

Center for World University Rankings
TOP 0.47% WORLDWIDE UNIVERSITY

Center for World University Rankings

2022

Why Choose this Program?

Fully Online or Hybrid Format
  • Choose between a fully online and hybrid mode of learning (start online, complete on-campus in the US).
  • Save USD 67,750 as compared to full-time US master’s (applicable for Hybrid).
  • Eligible for up to 3 years STEM OPT Visa in the US (applicable for Hybrid).
Learn from World-Class Faculty
  • Learn from University of Arizona faculty over live online classes and in-person classes (if on-campus).
  • Curriculum designed by leading experts to cover information science skills from fundamentals to complex, hands-on applications.
Read More
Practical, Hands-on Learning
  • Get the best practical learning experience with the faculty of The University of Arizona, a world leader in research and innovation.
  • Hands-on Projects and case studies to apply machine learning techniques to solve real-world business problems.
Read More
Working Professional
Choose between a fully online and hybrid mode of learning (start online, complete on-campus in the US).
Career Support
Save USD 67,750 as compared to full-time US master’s (applicable for Hybrid).
Salary Hike
Eligible for up to 3 years STEM OPT Visa in the US (applicable for Hybrid).
Salary Hike
Master's Degree from The University of Arizona
Learn More

Earn Master’s degree from One of the Top 100 Global Universities in the World, University of Arizona.

Online/Hybrid • 2 years

Top Recruiters that hire from University of Arizona

Google
Microsoft
TESLA
Goldman Sachs
Honeywell
BOEING
American Express
Apple
NASA

Career Opportunities and Earning Potential

Computer and Information Research Scientists

$130,000/year

uptrend

Machine Learning Engineer

$130,000/year

uptrend

Information Security Analysts

$110,000/year

uptrend

Data Scientist

$100,000/year

uptrend

Experience learning at a world-class campus

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Living on-campus at Chandler, Arizona

Chandler, home to more than 250,000 residents, is widely regarded as one of the top places to reside in Arizona. The City of Chandler's official website highlights the presence of some of the world's foremost employers within the city, such as Intel, Wells Fargo, and Bank of America.

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International Student Services

The International Student Services office strives to support all international students through graduation. You will find immigration guidance, academic resources, cultural connections, and social events through the International Student Services office.

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600+ Student Clubs and Organizations

There are hundreds of student clubs and organizations at the University of Arizona for everything from niche hobbies to extracurriculars to career-oriented and professional groups.

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Complete Access to the UArizona Libraries

A dozen state-of-the-art facilities are at your disposal. Hubs of technology, arts, sciences, and more make it easy to dive deep into knowledge.

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Top 10 Recreation Centers - Best Value Schools, 2021

University of Arizona’s Campus Recreation (Campus REC) is the health and well-being place to be. From individual and group fitness classes to outdoor adventures and aquatics, there is something for you no matter your level of ability or expertise.

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24X7 Technical Support

Access to technical support 24 hours a day, 7 days a week, will make you campus life easy.

Curriculum

The curriculum is designed by leading experts at the University of Arizona and practicing industry professionals. It is specifically curated to cover a range of information science skills - starting from the fundamentals and progressing to more complex, hands-on applications. The program ensures that learners without prior expertise in data science and machine learning too can gain the latest knowledge thereby making it one of the best Information Science programs.

  • Curriculum designed by The University of Arizona Faculty and industry Experts
  • 11 Projects including a Capstone Project to prepare you for real-world problems
Download Curriculum Download
Curriculum Snapshot
Foundations of Information
  • Sensing the Data

    • Learn about types, sources and management of internal and external data including static vs. streaming data, reports, databases and online data.

  • Data Collection

    • Learn the process of extracting, transforming and making data available for further use.

  • Data Usability

    • Learn the fundamentals of scoping the data by eliminating redundant elements and following logical, graphical, statistical analysis flow.

  • Data Storage

    • Get introduced to RDBMS, SQL, NoSQL, Data Marts, Data Lakes, ETL and Data Pipelines as well as large information repositories and tools like Hadoop, Hive, Spark.

Data Mining and Discovery
  • Introduction to Data Mining

    • Learn about data distributions and hypothesis testing. Use basic maths and common programming functions to handle data.

  • Business Problem Identification and Scoping

    • Map data to business problems being solved and select appropriate elements of data.

  • Graphical Data Analysis

    • Learn how to create basic graphs and analyze data visually. Use descriptive statistics to interpret data characteristics.

  • Unsupervised Learning Techniques

    • Use unsupervised learning like dimensionality reduction and clustering to discover elements of data.

Data Analysis and Visualization
  • Theory of Visualization

    • Learn about design, shapes and color theory behind visualizations.

  • Single and Multiple Dimension Visualizations

    • Use appropriate graphical design to depict information and integrate multiple elements of data to present compact and effective information.

  • Visualization for Audience

    • Learn about selection and presentation of visual information according to audience requirements.

  • Interactive Visualizations

    • Understand linking to databases, filtering and information highlighting for visualizations. Explore Python integrations with D3.js

Introduction to Machine Learning
  • Linear Modelling

    • Learn how to do linear regression and linear classification with perceptrons. Extend the linear model to various use cases.

  • Learning Theory and Model Evaluation

    • Understand bias-variance trade off and cross validation.

  • Probabilistic Methods

    • Learn about maximum likelihood and bayesian approach including Priors, Marginal Likelihood and Hyperparameters.

  • Optimization and Approximation Methods

    • Use Gradient Methods (gradient descent, Newton’s method), sampling and Markov Chain Monte Carlo Simulations (using metropolis hastings algorithm).

  • Classification Techniques

    • Implement basic classification techniques like Logistic Regression, Bayesian Classification, Naive Bayes, Nearest Neighbors, Support Vector Machines.

  • Domain Specific Techniques

    • Understand Nonparametric Bayesian Methods, Gaussian Processes, Topic Modelling, Ensembles, Boosting and Random Forests.

Data Warehousing and Analytics in the Cloud
  • Introduction to Cloud

    • Learn Cloud Computing basics for Machine Learning and Data Science. Explore Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Function as a Service (FaaS) in the context of MS Azure.

  • Setting up Cloud and Parallel Processing

    • Learn about Cloud storage systems and virtualization. Use parallel programming tools and infrastructure in the cloud like HDFS, Hive, SparkML.

  • Data Warehousing

    • Develop data warehouses in the cloud. Develop and deploy Machine Learning models in the cloud.

  • Data Warehouse Design

    • Use SQL and NoSQL Data Warehouse to understand designing and querying Data Warehouses.

Text Retrieval and Web Search
  • Introduction to Text Retrieval

    • Learn about boolean retrieval, term vocabulary, posting lists dictionaries and tolerant retrieval.

  • Vector Space Modelling

    • Understand index construction, index compression, scoring, term weighting and development of the vector space model.

  • Information Retrieval

    • Learn about evaluation in information retrieval, probabilistic information retrieval and language models for information retrieval.

  • Text Classification and Clustering

    • Learn about techniques like text classification and Naive Bayes, Vector Space Classification along with Flat Clustering and Hierarchical Clustering.

  • Text Handling Techniques

    • Learn about Expectation Maximization Algorithm, Latent Semantics Indexing, Probabilistic Latent Semantic Analysis and Latent Dirichlet allocation.

  • Web Search Basics

    • Learn about web crawling and indexes and understand link analysis on the internet.

Neural Networks
  • Introduction to Neural Networks

    • Learn about history, development and applications of Neural Networks.

  • Feed Forward Neural Networks

    • Learn about Neural Network architecture, gradient based learning and back propagation.

  • Regularization

    • Understand dropout, parameter penalties and early stopping.

  • CNN and RNN

    • Understand Convolution and Pooling. Apply gated recurrent network along with encoder-decoder network and understand transformer network.

  • Practical Considerations and Interpretability

    • Understand default baseline models and debugging and use local surrogates and saliency maps for interpretability.

Applied Natural Language Processing
  • Distribution Similarity

    • Understand basic count-based methods and word embeddings.

  • Sequence Models

    • Learn about HMM, MEMM, LSTM, and applications to part-of-speech tagging and information extraction.

  • Structured Learning

    • Implement cases of shift-reduce algorithms, PCFG, tree LSTM, and applications to syntactic parsing.

  • Alignment Models

    • Understand alignment models and applications to machine translation and question answering.

  • Advanced Techniques

    • Learn about reading comprehension, question answering and summarization.

Artificial Intelligence and Machine Learning
  • Computer Vision Application of Machine Learning

    • Understand image formats, image manipulation, Machine Learning and Neural Networks to classify images.

  • CNN Architectures and Transfer Learning

    • Implement latest CNN architectures with transfer learning as well as object classification use cases by deploying latest techniques including VGG16, ResNet60.

  • Object Detection

    • Understand object detection approaches like brute force, sliding window, regional proposal and implement latest algorithms like faster RCNN, SSD, YOLO.

  • Generative Modelling

    • Use Generative Adversarial Networks (GANs) for industry-related applications like style transfer and text-to-image conversion.

Capstone Project
    *Capstone projects should be aligned with the research priorities of the university. Some examples include:-
  • Predicting climate change patterns using hydrological data from regions.
  • Predicting climatic vulnerability based on historical data.
  • Using NLP in analysis of public health records and predicting population health parameters.
  • Using non-invasive imaging techniques to rapidly assess population health.
    • *Actual capstone projects will be assigned by the program faculty.
Master's Degree from The University of Arizona

Upon the successful completion of Information Science with Specialization in Machine Learning program, you would receive a Master’s Degree from The University of Arizona.

Languages and Tools covered

Languages and more...
Languages

Meet the Faculty

Learn from the esteemed faculty at University of Arizona and practicing Industry Professionals with immense experience in the field of Information Science.

Professor Dr.Steven Bethard

Dr.Steven Bethard

Associate Professor, School of Information

Professor Dr. Christian Roman-Palacios

Dr. Christian Roman-Palacios

Assistant Professor of Practice, School of Information

Professor Dr. Hong Cui

Dr. Hong Cui

Professor, School of Information

Professor Dr. Bryan Heidorn

Dr. Bryan Heidorn

Professor, School of Information
Director, Center for Digital Society and Data Studies.

Professor Dr. Meaghan Wetherell

Dr. Meaghan Wetherell

Assistant Professor of Practice, School of Information

Professor Dr. Peter Jansen

Dr. Peter Jansen

Assistant Professor, School of Information

Get The University of Arizona Advantage

  • Bear Down Network

    Get access to the online directory of The University of Arizona alumni with features like job board, mentorship opportunities, and much more.

  • Wildcat Mentor Society

    Join transformative program to build strong professional network and get guidance from mentors.

  • Graduate Center

    A reliable resource for graduate and postdoctoral learners to enhance their academic and professional success.

  • Lifelab

    Personalized career guidance platform to help learners with resume-building, cover letters, LinkedIn profiles, and more.

  • Handshake

    Official job board and campus interviewing system for learners to access hiring opportunities, career prep resources, and communities.

  • On-Campus Experience

    Interact with top-notch faculty and global peers at the University of Arizona campus in Tucson, Arizona.

Great Learning Advantage

Learn from a comprehensive curriculum taught by world-class faculty. Get guidance on your learning journey, and access dedicated career support.

DESIGNED FOR PROFESSIONALS/FRESHERS

Learn while pursuing your career

  • Online and hybrid formats with mentorship provided throughout the master's degree
  • Practical Insights from Industry Experts

STRUCTURED PROGRAM WITH GUIDANCE

Networking and Program support

  • Dedicated program manager to solve your queries
  • Mentorship from experts to gain industry insights
  • Interact with peers to grow your professional network

Learner Testimonials*

Ankit Kumar Sharma - Learner
Ankit Kumar Sharma

Software Dev 3

The program covered a comprehensive range of concepts and the explanation provided were very clear and easy to understand. The inclusion of examples and case studies helped to illustrate complex topics and provided practical insights into real world applications.The instructors were highly responsive and supportive, actively encouraging interaction and clearing doubts effectively. I would highly recommend it to anyone looking for further knowledge and skills in this particular field.  

Soundarya - Learner
Soundarya

Ind & Function AI Decision Science Analyst

We've had access to all the necessary learning resources required for the program, and both the industry and faculty sessions have been exceptionally valuable and interactive. I'm particularly pleased with how the curriculum facilitates learning new things especially the inclusion of Gen Al - related topics. I also appreciate your approachability and responsiveness in addressing our concerns then and there.  

Prashanth Shenoy - Learner
Prashanth Shenoy

L&TD Professional at a Fortune 500 Fintech Company

To me, the MSML program stands out as the best offering in the market for anyone looking to advance in Al & ML.It's a well-crafted program that respects the time and commitments of working professionals while pushing us towards mastery in our field. I highly recommend it to anyone looking to make a mark in machine learning, especially the program's practical approach and how it seamlessly integrates into a busy professional's life.

David Phillips - Learner
David Phillips

Master of Science in Information Science

The program is delightful, and the professors taught me so much. I have absolutely enjoyed the program.

Loren Champlin - Learner
Loren Champlin

PhD student in Information Science

I found the research in AI, game design and ML really insightful. The faculty here finds time to talk to learners and has you best interests at heart.

Sarah Stueve - Learner
Sarah Stueve

PhD student in Information Science

I was exposed to different projects in a breadth of areas. The University and Program caters to people from different backgrounds.

*Testimonials from learners of School Of Information, University Of Arizona

Fees and Application Details

MS in Information Science: Machine Learning Program

Program
Fees

Online

USD 11,000

Hybrid (Online+On-Campus)*

USD 34,176

*Save USD 67,750 as compared to full-time US master’s

Earn Master’s Degree from

  • 2 years Program
  • Choose between a fully online and hybrid mode of learning ( first year online, second year on-campus)
  • Comprehensive curriculum with hands-on learning and mentorship sessions
  • Quick application with no additional tests or prerequisites
  • Globally recognized Master’s degree from the University of Arizona
Apply Now

Application Process

1

Apply Online

Complete filling a fast and easy online application form. No additional tests or prerequisites are needed to apply.

2

Pre-screening

Our team will make contact with you by phone to confirm your eligibility for the program.

3

Application Assessment

The Admissions team will assess your application and provide a timely response.

4

Join The Program

If selected, you will receive an acceptance letter with instructions on how to pay and join the program.

Great Learning provides end-to-end support in applying to the University of Arizona and also in the US visa application process during the first year (for hybrid students).

Who is this program for?

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Early Career Professionals

  • Young, early-career professionals looking to go abroad and master information-driven innovation with knowledge of the latest information methods.
  • Young, early-career professionals looking to go abroad and master information-driven innovation with knowledge of the latest information methods.
Curated Jobs
Curated Jobs

Mid & Senior Level Professionals

  • Mid and senior-level professionals who are looking to go abroad or learn online and stay up-to-date with the latest information management skills needed for success in the digital world.
  • Mid and senior-level professionals who are looking to go abroad or learn online and stay up-to-date with the latest information management skills needed for success in the digital world.
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Working Professionals

  • Professionals who wish to learn online without quitting their job and transition to an information management career with industry-ready skills and knowledge.
  • Professionals who wish to learn online without quitting their job and transition to an information management career with industry-ready skills and knowledge.

Upcoming Application Deadline

Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats.

Deadline: 26th Nov 2024

Apply Now

Batch Start Dates

Great Learning-Online Center

Online/Hybrid

To be announced

Still have queries?
Contact Us

Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at msis.uoa@mygreatlearning.com or +1 520 341 2109.

Application Closes 26th Nov 2024

Download Brochure

Check out the program and fee details in our brochure

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Master Computer Science Technology: MS in Information Science - Machine Learning

The Master of Science in Information Science with a specialization in Machine Learning, offered in collaboration between the University of Arizona and Great Learning, marks a significant advancement in data science and information management. This Masters in Information Science program is meticulously crafted to address the escalating demand for professionals adept at interpreting and managing the extensive data prevalent in today's computer science and information technology landscape. It is a parallel educational pathway to MS in Computer Science. It imparts skills in designing data-driven solutions and developing forward-thinking management strategies, rendering its graduates highly valuable in the contemporary job market.

Reasons to Opt for the Masters in Machine Learning Program

The MS in Machine Learning program is a gateway to numerous opportunities. It is particularly advantageous for international students eager to explore the complexities of data and machine learning through an online program, combining theoretical foundations with practical implementation through the information science master program.

The program is ideally suited for early-career professionals seeking to excel in data-driven innovation and for mid to senior-level professionals aiming to keep pace with evolving trends in information management. This MS course duration, format, and structure also suit working professionals to upskill while practicing in the industry. Its emphasis on real-world applications ensures graduates are prepared to address current industry challenges effectively.

Program Overview and Format

This two-year graduate program is delivered in a hybrid format, blending online and on-campus instruction in the United States. Notable features include a substantial cost saving of over INR 55 lakhs and eligibility for up to three years of STEM OPT visa in the US.

The curriculum encompasses core principles, advanced natural language processing, and neural network applications in machine learning, incorporating 11 practical projects through various programming languages and case studies, culminating in a capstone project. The initial year is conducted online, followed by on-campus learning MS in USA at the University of Arizona's dynamic Tucson campus in the second year.

About the University of Arizona

Founded in 1885, the University of Arizona is a globally acclaimed institution, ranked among the Top 100 Best Global Universities by US News and World Report (2022). As a distinguished Research 1 university, it boasts annual research expenditures of $734 million and is celebrated for its cross-disciplinary scholarship and entrepreneurial collaborations. The university's robust emphasis on research and innovation renders it an exemplary setting for pursuing an MS in Information Science: Machine Learning. It also popularly hosts higher education degrees like online Masters in Computer Science and other programs.

Career Opportunities and Industry Alignment

The program equips graduates for diverse roles in the rapidly evolving domains of software engineering, data analytics, data science and artificial intelligence. Its comprehensive curriculum, enriched with industry expert insights, prepares learners to solve real-world business challenges using machine learning techniques. Consequently, graduates are highly sought after in various sectors, including technology, finance, healthcare, and e-commerce.

Admissions and Tuition

The admission process is streamlined, with no TOEFL/GRE score requirement. Prospective learners undergo a pre-screening call and application review. The program's tuition is competitively priced, offering a cost-effective alternative to traditional full-time master's programs in the US. Scholarships and financing options are available, making this program feasible for many aspiring professionals.

Support from Great Learning

Great Learning offers extensive support to learners enrolled in the MS in Information Science: Machine Learning program. This includes pre-course work to strengthen foundational knowledge, additional sessions with industry experts, and continuous academic support from a dedicated program team. Great Learning also assists with the visa application process, providing essential support in document preparation and interview preparation. This comprehensive support system ensures that learners excel academically and are well-prepared for their professional career paths post-graduation.

The Master of Science in Information Science: Machine Learning program at the University of Arizona, in collaboration with Great Learning, is a comprehensive and forward-thinking educational pathway. It parallels the rigor and depth of a Master of Science in Computer Science while integrating critical aspects of a Master of Information Technology and Information Systems.

The Machine Learning hybrid program is uniquely positioned to equip professionals with the in-demand skill set and knowledge necessary to excel in the ever-evolving landscape of information systems and machine learning, preparing them for an expanding and dynamic career in various industries